Estimating chromosomal copy numbers from Affymetrix SNP & CN chips

Estimating chromosomal copy numbers

from Affymetrix SNP & CN chips

Henrik Bengtsson & Terry SpeedDept of Statistics, UC Berkeley

September 13, 2007

"Statistics and Genomics Seminar"

Size = 264 kb, Number of SNPs = 72

What are copy numbers and segmentation?

Objectives

• Total copy number estimation/segmentation• Estimate single-locus CNs well

(segmentation method takes it from there)

• All generations of Affymetrix SNP arrays:– SNP chips: 10K, 100K, 500K– SNP & CN chips: 5.0, 6.0

• Small and very large data sets

Available in aroma.affymetrix

“Infinite” number of arrays: 1-1,000sRequirements: 1-2GB RAMArrays: SNP, exon, expression, (tiling).Dynamic HTML reportsImport/export to existing methodsOpen source: RCross platform: Windows, Linux, Mac

Acknowledgments

WEHI, Melbourne:Ken Simpson

UC Berkeley:James BullardKasper HansenElizabeth Purdom

ISREC, Lausanne:“Asa” Wirapati

John Hopkins:Benilton CarvalhoRafael Irizarry

Affymetrix, California:Ben BolstadSimon CawleyLuis JevonsChuck Sugnet

Affymetrix chips

Generic Affymetrix chip

1.28 cm

6.5 million probes/ chip

1.28 cm

Feature size: 100µm to 18µm to 11µm and now 5µm.Soon: 1µm, 0.8µm, with a huge increase in number of probes.

*

5 µm

5 µm

> 1 million identical 25bp sequences

* ***

Abbreviated generic assay description

1. Start with target gDNA (genomic DNA) or mRNA.

2. Obtain labeled single-stranded target DNA fragments for hybridization to the probes on the chip.

3. After hybridization, washing, staining and scanning we get a digital image. This is summarized across pixels to probe-level intensities before we begin. They are our raw data.

Affymetrix probe terminology

Target DNA: ...CGTAGCCATCGGTAAGTACTCAATGATAG... |||||||||||||||||||||||||

Perfect match (PM): ATCGGTAGCCATTCATGAGTTACTAMis-match (MM): ATCGGTAGCCATACATGAGTTACTA

25 nucleotides

* *

* **

PM

Target seq.

* **

MM

* **

other PMs

Other DNA Other DNA Other seq.

X

Affymetrix SNP chips(Mapping 10K, 100K, 500K)

Single Nucleotide Polymorphism (SNP)

Definition:A sequence variation such that two genomes may differ by a single nucleotide (A, T, C, or G).

Allele A: A...CGTAGCCATCGGTA/GTACTCAATGATAG...

Allele B: G

A person is either AA, AB, or BB at this SNP.

Probes for SNPs

PMA: ATCGGTAGCCATTCATGAGTTACTAAllele A: ...CGTAGCCATCGGTAAGTACTCAATGATAG...

Allele B: ...CGTAGCCATCGGTAGGTACTCAATGATAG...PMB: ATCGGTAGCCATCCATGAGTTACTA

(Also MMs, but not in the newer chips, so we will not use these!)

* **

PMA >> PMB

AA* **

*

* **

PMA << PMB

* **

BB* **

PMA ¼ PMB

AB* **

Affymetrix SNP & CN chips(Genome-Wide Human SNP Array 5.0 & 6.0)

Copy-number/non-polymorphic probes (CNPs)

CN locus: ...CGTAGCCATCGGTAAGTACTCAATGATAG...PM: ATCGGTAGCCATTCATGAGTTACTA

* **

PM = c

CN=1* **

PM = 2¢c

CN=2* **

PM = 3¢c

CN=3

Genome-Wide Human SNP Array 6.0includes frequently requested properties

• > 906,600 SNPs:– Unbiased selection of 482,000 SNPs:

historical SNPs from the SNP Array 5.0 (== 500K)– Selection of additional 424,000 SNPs:

• Tag SNPs• SNPs from chromosomes X and Y• Mitochondrial SNPs• Recent SNPs added to the dbSNP database• SNPs in recombination hotspots

• > 946,000 copy number probes (CNPs):– 202,000 probes targeting 5,677 CNV regions from the Toronto

Database of Genomic Variants. Regions resolve into 3,182 distinct, non-overlapping segments; on average 61 probe sets per region

– 744,000 probes, evenly spaced along the genome

Large increase in density

6.0

5.0

500K

100K

10K

1.6kb

3.6kb

6.0kb

26kb

4£ further out…

294kb

year

# loci

History of SNP & CNP chipsAffymetrix & Illumina are competing

10K 100K 500K 5.0 6.0

Released July 2003 April 2004 Sept 2005 Feb 2007 May 2007

# SNPs 10,204 116,204 500,568 500,568 934,946

# CNPs - - - 340,742 946,371

# loci 10,204 116,204 500,568 841,310 1,878,317

Distance 294kb 25.8kb 6.0kb 3.6kb 1.6kb

Price / chip set 65 USD 400 USD 260 USD 175 USD 300 USD

# loci / cup of espresso ($1.35)

116 loci 216 loci 1426 loci 3561 loci 4638 loci

Price source: Affymetrix Pricing Information, http://www.affymetrix.com/, September 2007.

Copy-number analysis with SNP arrays

(10K, 100K, 500K)

SNP chips can be used to determine copy number

Some sample figures based on a 250K SNP chip showing deletions and amplifications

Size = 424 kb, Number of SNPs = 118 Results using of dChip and GLAD.

Size = 168 kb, Number of SNPs = 55

Our method:

CRMA(10K, 100K, 500K)

Copy-number estimation using Robust Multichip Analysis (CRMA)

CRMA

Preprocessing(probe signals)

allelic crosstalk (or quantile)

Total CN PM = PMA + PMB

Summarization (SNP signals )

log-additivePM only

Post-processing fragment-length

(GC-content)

Raw total CNs R = Reference

Mij = log2(ij /Rj) chip i, probe j


CRMA


allelic crosstalk (quantile)

Total CNs PM=PMA+PMB


log-additive(PM-only)


(GC-content)

Raw total CNs Mij = log2(ij/Rj)

Cross-hybridization:

Allele A: TCGGTAAGTACTCAllele B: TCGGTATGTACTC

AA* **

PMA >> PMB

* **

* **

PMA ¼ PMB

AB* ** *

* **

PMA << PMB

* **

BB

AA

TTAT


CRMA







(GC-content)


offset

+

PMT

PMA


CRMA







(GC-content)


PMT

PMA


CRMA







(GC-content)


Crosstalk calibration corrects for differences in distributions too

log2 PM


CRMA







(GC-content)


AA* **

PM = PMA + PMB

* **

* **

PM = PMA + PMB

AB* **

*

* **

PM = PMA + PMB

* **

BB


CRMA







(GC-content)


The log-additive model:

log2(PMijk) = log2ij + log2jk + ijk

sample i, SNP j, probe k.

Fit using robust linear models (rlm)

100K


CRMA







(GC-content)


Longer fragments ) less amplified by PCR ) weaker SNP signals

500K


CRMA







(GC-content)


Longer fragments ) less amplified by PCR ) weaker SNP signals


CRMA







(GC-content)


Normalize to get samefragment-length effect for all hybridizations


CRMA







(GC-content)


Comparison(other methods)

Other methods

CRMA dChip(Li & Wong 2001)

CNAG(Nannya et al 2005)

CNAT v4(Affymetrix 2006)



invariant-set scale quantile

Total CNs PM=PMA+PMB PM=PMA+PMB

MM=MMA+MMB

PM=PMA+PMB =A+B



multiplicative(PM-MM)

sum (PM-only)

log-additive (PM-only)


(GC-content)

- fragment-length

GC-content

fragment-length

GC-content

Raw total CNs Mij = log2(ij/Rj) Mij = log2(ij/Rj) Mij = log2(ij/Rj) Mij = log2(ij/Rj)

How well can be differentiate between one and two copies?

HapMap (CEU):Mapping250K Nsp data30 males and 29 females (no children; one excl. female)

Chromosome X is known: Males (CN=1) & females (CN=2)5,608 SNPs

Classification rule:

Mij < threshold ) CNij =1, otherwise CNij =2.Number of calls: 595,608 = 330,872

Calling samples for SNP_A-1920774

# males: 30# females: 29

Call rule:If Mi < threshold, a male

Calling a male male:#True-positives: 30 TP rate: 30/30 = 100%

Calling a female male:#False-positive : 5FP rate: 5/29 = 17%

Receiver Operator Characteristic (ROC)

FP rate(incorrectly calling females male)

TP rate

(correctly calling a males male)

increasingthreshold

²

(17%,100%)

Single-SNP comparisonA random SNP

TP rate



Single-SNP comparisonA non-differentiating SNP

TP rate



Performance of an average SNPwith a common threshold

59 individuals £

CRMA & dChip perform betterfor an average SNP (common threshold)

Number of calls:59£5,608 = 330,872

TP rate



0.85

1.00

0.150.00

Zoom in

CRMA

dChip

CNAG

CNAT

"Smoothing"

No averaging (R=1)Averaging two and two (R=2)Averaging three and three (R=3)

Average across SNPsnon-overlapping windows

threshold

A false-positive(or real?!?)

Better detection rate when averaging(with risk of missing short regions)

R=1(no avg.)

R=2

R=3

R=4

CRMA does better than dChip

CRMA

dChip

CRMA does better than dChip

CRMA

dChipControl for FP rate: 1.0%

CRMA dChipR=1 69.6% 63.1%R=2 96.0% 93.8%R=3 98.7% 98.0%R=4 99.8% 99.6%… … …

²

²

²²

²

²

Comparing methods by “resolution”controlling for FP rate

@ FP rate: 1.0%

CRMA

CNAT

²

²² ² ² ² ²

dChipCNAG

Early work:

CRMA 6(SNP 5.0 & 6.0 chips)

CRMA with CN probes

CRMA


allelic crosstalk

(or quantile)

Total CN SNPs: PM = PMA + PMB

CNs: PM


single-arrayaveraging


(GC-content)

Raw total CNs

R = Reference

Mij = log2(ij /Rj) chip i, probe j

Allelic crosstalk calibration-incorporating CN probes

SNPs:For each allele pair in {AC, AG, AT, CG, CT, GT}:

1) Estimate crosstalk model:offset: aSNP = (aA, aB)crosstalk matrix: S = [SAA, SAB; SBA, SBB]

2) Calibrate probe pairs {PM} = {(PMA, PMB)}:PM' Ã S-1 (PM - aSNP)

3) Rescale {PM'A} and {PM'B} to have average 2200.

CN probes:1) Calculate the average offset across all alleles:

offset: aCN = 1/6 * k {wk*(aA+aB)/2},with weights wk corresponding to n:s (above).

2) Calibrate CN probes {y}:PM' Ã PM - aCN

3) Rescale {PM'} to have average 2200.

Probe-level modelling (PLM)

SNPs:* Technical replicates:

PMA = (PMA1, PMA2, PMA3) and PMB = (PMB1, PMB2, PMB3)

All should have the same probe affinities => No probe-affinity model(!)

* Suggestion:PMA = median {PMAk}PMB = median {PMBk}PM = PMA + PMB (compare to CN probes!) = PM

CN probes:* (Mostly) single probe units, i.e. nothing much to do;

= PM

CRMA with and without CN probes

CRMA

(SNP)

CRMA6

(SNP & CN)




Summarization (locus signals )

Total CN:

PM=PMA+PMB


= "chip effects"

Averaging SNPs:

PMA = median{PMA}

PMB = median{PMB}

Total CN:

PM = PMA + PMB

= PM


(GC-content)

fragment-length

(GC-content)

Raw total CNs Mij = log2(ij/Rj) Mij = log2(ij/Rj)

Comparisonacross generations

(100K - 500K - 6.0)

Average-locus ROC100K ! 500K ! 6.0

100K:Hind240, Xba240 & both

500K:Nsp, Sty & both

6.0:SNP, CN probes & all

Resolution comparison- at 1.0% FP

At any given resolution (kb), we have:

TP6.0,SNP > TP500K > TP100K

Note, the differences may be due to lab effects (the HapMap 100K, 500K & 6.0 hybridization

were done in different years/labs).In either case, the trend is in the right direction.

Resolution comparison- at 1.0% FP

Summary

Conclusions

• It helps to:– Control for allelic crosstalk.– Sum alleles at PM level: PM = PMA + PMB.– Control for fragment-length effects.

• Resolution: 6.0 (SNPs) > 500K > 100K(or lab effects).

• Currently estimates from CN probes are poor. Not unexpected. Better preprocessing might help.

Appendix

Density of TP rates whencontrolling for FP rate (5,608 SNPs)

TP rate(correctly calling males male)

FP rate: 1.0% (incorrectly calling females male)

CNAT: 10% poor SNPs

density

CRMA

dChip

CNAG

CNAT

Effect of different normalization steps

Allelic-crosstalkcalibration +Fragment-lengthnorm.

Allelic-crosstalkcalibration

Quantilenormalization

Estimating chromosomal copy numbers from Affymetrix SNP & CN chips

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